Feature selection in text categorization using the Baldwin effect

Edmund S. Yu, Elizabeth D Liddy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Text categorization is the problem of automatically assigning predefined categories to natural language texts. A major difficulty of this problem stems from the high dimensionality of its feature space. Reducing the dimensionality, or selecting a good subset of features, without sacrificing accuracy, is of great importance for neural networks to be successfully applied to this area. In this paper, we propose a neuro-genetic approach to feature selection in text categorization. Candidate feature subsets are evaluated by using three-layer feedforward neural networks. The Baldwin effect concerns the tradeoffs between learning and evolution. It is used in our research to guide and improve the GA-based evolution of the feature subsets. Experimental results show that our neuro-genetic algorithm is able to perform as well as, if not better than, the best results of neural networks to date, while using fewer input features.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherIEEE Computer Society
Pages2924-2927
Number of pages4
Volume4
StatePublished - 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period7/10/997/16/99

ASJC Scopus subject areas

  • Software

Fingerprint Dive into the research topics of 'Feature selection in text categorization using the Baldwin effect'. Together they form a unique fingerprint.

  • Cite this

    Yu, E. S., & Liddy, E. D. (1999). Feature selection in text categorization using the Baldwin effect. In Proceedings of the International Joint Conference on Neural Networks (Vol. 4, pp. 2924-2927). IEEE Computer Society.